Research on urban public bicycle dispatching optimization method

Unreasonable public bicycle dispatching area division seriously affects the operational efficiency of the public bicycle system. To solve this problem, this paper innovatively proposes an improved community discovery algorithm based on multi-objective optimization (CDoMO). The data set is preprocessed into a lease/return relationship, thereby it calculated a similarity matrix, and the community discovery algorithm Fast Unfolding is executed on the matrix to obtain a scheduling scheme. For the results obtained by the algorithm, the workload indicators (scheduled distance, number of sites, and number of scheduling bicycles) should be adjusted to maximize the overall benefits, and the entire process is continuously optimized by a multi-objective optimization algorithm NSGA2. The experimental results show that compared with the clustering algorithm and the community discovery algorithm, the method can shorten the estimated scheduling distance by 20%-50%, and can effectively balance the scheduling workload of each area. The method can provide theoretical support for the public bicycle dispatching department, and improve the efficiency of public bicycle dispatching system.

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Urban public bicycle dispatching optimization method

PeerJ Computer Science ◽

10.7717/peerj-cs.224 ◽

2019 ◽

Vol 5 ◽

pp. e224

Author(s):

Fei Lin ◽

Yang Yang ◽

Shihua Wang ◽

Yudi Xu ◽

Hong Ma ◽

...

Keyword(s):

Clustering Algorithm ◽

Optimization Method ◽

Multi Objective Optimization ◽

Data Set ◽

Community Discovery ◽

The Public ◽

Multi Objective ◽

Scheduling Scheme ◽

Theoretical Support ◽

The Matrix

Unreasonable public bicycle dispatching area division seriously affects the operational efficiency of the public bicycle system. To solve this problem, this paper innovatively proposes an improved community discovery algorithm based on multi-objective optimization (CDoMO). The data set is preprocessed into a lease/return relationship, thereby it calculated a similarity matrix, and the community discovery algorithm Fast Unfolding is executed on the matrix to obtain a scheduling scheme. For the results obtained by the algorithm, the workload indicators (scheduled distance, number of sites, and number of scheduling bicycles) should be adjusted to maximize the overall benefits, and the entire process is continuously optimized by a multi-objective optimization algorithm NSGA2. The experimental results show that compared with the clustering algorithm and the community discovery algorithm, the method can shorten the estimated scheduling distance by 20%–50%, and can effectively balance the scheduling workload of each area. The method can provide theoretical support for the public bicycle dispatching department, and improve the efficiency of public bicycle dispatching system.

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An adaptive RBF neural network–based multi-objective optimization method for lightweight and crashworthiness design of cab floor rails using fuzzy subtractive clustering algorithm

Structural and Multidisciplinary Optimization ◽

10.1007/s00158-020-02797-9 ◽

2021 ◽

Author(s):

Dengfeng Wang ◽

Chong Xie ◽

Shuang Wang

Keyword(s):

Neural Network ◽

Clustering Algorithm ◽

Rbf Neural Network ◽

Optimization Method ◽

Subtractive Clustering ◽

Multi Objective Optimization ◽

Multi Objective ◽

Crashworthiness Design

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A Design of a Simple Evolutionary Multi-objective Optimization Method Based on the Combined Use of Scalarization and Particle Swarm Optimization

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.132.42 ◽

2012 ◽

Vol 132 (1) ◽

pp. 42-52

Author(s):

Kazuaki Masuda ◽

Kazuaki Harada ◽

Kenzo Kurihara

Keyword(s):

Particle Swarm Optimization ◽

Particle Swarm ◽

Optimization Method ◽

Multi Objective Optimization ◽

Swarm Optimization ◽

Multi Objective ◽

Combined Use

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Multi-objective Optimization Method for Distribution System Configuration using Pareto Optimal Solution

IEEJ Transactions on Power and Energy ◽

10.1541/ieejpes.128.1208 ◽

2008 ◽

Vol 128 (10) ◽

pp. 1208-1216 ◽

Cited By ~ 3

Author(s):

Yasuhiro Hayashi ◽

Hirotaka Takano ◽

Junya Matsuki ◽

Yuji Nishikawa

Keyword(s):

Distribution System ◽

Optimal Solution ◽

Optimization Method ◽

Pareto Optimal Solution ◽

Pareto Optimal ◽

Multi Objective Optimization ◽

System Configuration ◽

Multi Objective

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Student Admission Assesment using Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA)

10.31227/osf.io/gp4wj ◽

2018 ◽

Author(s):

Rivalri Kristianto Hondro ◽

Mesran Mesran ◽

Andysah Putera Utama Siahaan

Keyword(s):

Decision Support ◽

Decision Support Systems ◽

Support Systems ◽

Selection Process ◽

Initial Step ◽

Optimization Method ◽

Ratio Analysis ◽

Multi Objective Optimization ◽

Prospective Students ◽

Multi Objective

Procurement selection process in the acceptance of prospective students is an initial step undertaken by private universities to attract superior students. However, sometimes this selection process is just a procedural process that is commonly done by universities without grouping prospective students from superior students into a class that is superior compared to other classes. To process the selection results can be done using the help of computer systems, known as decision support systems. To produce a better, accurate and objective decision result is used a method that can be applied in decision support systems. Multi-Objective Optimization Method by Ratio Analysis (MOORA) is one of the MADM methods that can perform calculations on the value of criteria of attributes (prospective students) that helps decision makers to produce the right decision in the form of students who enter into the category of prospective students superior.

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Multi-objective optimization of optimal capacitor allocation in radial distribution systems

International Journal of Emerging Electric Power Systems ◽

10.1515/ijeeps-2019-0206 ◽

2020 ◽

Vol 21 (3) ◽

Author(s):

Sayed Mir Shah Danish ◽

Mikaeel Ahmadi ◽

Atsushi Yona ◽

Tomonobu Senjyu ◽

Narayanan Krishna ◽

...

Keyword(s):

Radial Distribution ◽

Distribution System ◽

Distribution Systems ◽

Reactive Power ◽

Stability Index ◽

Distribution Networks ◽

Optimization Method ◽

Optimal Size ◽

Multi Objective Optimization ◽

Multi Objective

AbstractThe optimal size and location of the compensator in the distribution system play a significant role in minimizing the energy loss and the cost of reactive power compensation. This article introduces an efficient heuristic-based approach to assign static shunt capacitors along radial distribution networks using multi-objective optimization method. A new objective function different from literature is adapted to enhance the overall system voltage stability index, minimize power loss, and to achieve maximum net yearly savings. However, the capacitor sizes are assumed as discrete known variables, which are to be placed on the buses such that it reduces the losses of the distribution system to a minimum. Load sensitive factor (LSF) has been used to predict the most effective buses as the best place for installing compensator devices. IEEE 34-bus and 118-bus test distribution systems are utilized to validate and demonstrate the applicability of the proposed method. The simulation results obtained are compared with previous methods reported in the literature and found to be encouraging.

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A Multi-Objective Optimization Method for Hospital Admission Problem—A Case Study on Covid-19 Patients

Algorithms ◽

10.3390/a14020038 ◽

2021 ◽

Vol 14 (2) ◽

pp. 38

Author(s):

Amr Mohamed AbdelAziz ◽

Louai Alarabi ◽

Saleh Basalamah ◽

Abdeltawab Hendawi

Keyword(s):

Medical Care ◽

Real Life ◽

Optimization Method ◽

Multi Objective Optimization ◽

Admission Process ◽

Multi Objective ◽

Admission Time ◽

King Faisal Specialist Hospital ◽

Number Of Patients ◽

Massive Number

The wide spread of Covid-19 has led to infecting a huge number of patients, simultaneously. This resulted in a massive number of requests for medical care, at the same time. During the first wave of Covid-19, many people were not able to get admitted to appropriate hospitals because of the immense number of patients. Admitting patients to suitable hospitals can decrease the in-bed time of patients, which can lead to saving many lives. Also, optimizing the admission process can minimize the waiting time for medical care, which can save the lives of severe cases. The admission process needs to consider two main criteria: the admission time and the readiness of the hospital that will accept the patients. These two objectives convert the admission problem into a Multi-Objective Problem (MOP). Pareto Optimization (PO) is a common multi-objective optimization method that has been applied to different MOPs and showed its ability to solve them. In this paper, a PO-based algorithm is proposed to deal with admitting Covid-19 patients to hospitals. The method uses PO to vary among hospitals to choose the most suitable hospital for the patient with the least admission time. The method also considers patients with severe cases by admitting them to hospitals with the least admission time regardless of their readiness. The method has been tested over a real-life dataset that consisted of 254 patients obtained from King Faisal specialist hospital in Saudi Arabia. The method was compared with the lexicographic multi-objective optimization method regarding admission time and accuracy. The proposed method showed its superiority over the lexicographic method regarding the two criteria, which makes it a good candidate for real-life admission systems.

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Auto-sharing parameters for transfer learning based on multi-objective optimization

Integrated Computer-Aided Engineering ◽

10.3233/ica-210655 ◽

2021 ◽

pp. 1-13

Author(s):

Hailin Liu ◽

Fangqing Gu ◽

Zixian Lin

Keyword(s):

Transfer Learning ◽

Optimization Problem ◽

Data Sets ◽

Multi Objective Optimization ◽

Particle Swarm Optimizer ◽

Real World Data ◽

Data Set ◽

Target Task ◽

Main Research ◽

Multi Objective

Transfer learning methods exploit similarities between different datasets to improve the performance of the target task by transferring knowledge from source tasks to the target task. “What to transfer” is a main research issue in transfer learning. The existing transfer learning method generally needs to acquire the shared parameters by integrating human knowledge. However, in many real applications, an understanding of which parameters can be shared is unknown beforehand. Transfer learning model is essentially a special multi-objective optimization problem. Consequently, this paper proposes a novel auto-sharing parameter technique for transfer learning based on multi-objective optimization and solves the optimization problem by using a multi-swarm particle swarm optimizer. Each task objective is simultaneously optimized by a sub-swarm. The current best particle from the sub-swarm of the target task is used to guide the search of particles of the source tasks and vice versa. The target task and source task are jointly solved by sharing the information of the best particle, which works as an inductive bias. Experiments are carried out to evaluate the proposed algorithm on several synthetic data sets and two real-world data sets of a school data set and a landmine data set, which show that the proposed algorithm is effective.

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